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AI in Drug Discovery

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: AI in Drug Discovery
Περιγραφή: This open Access book constitutes the refereed proceedings of the First International Workshop on AI in Drug Discovery, AIDD 2024, held as a part of the 33rd International Conference on Artificial Neural Networks, ICANN 2024, in Lugano, Switzerland, on September 19, 2024. The 12 papers presented here were carefully reviewed and selected for these open access proceedings. These papers focus on various aspects of the rapidly evolving field of Artificial Intelligence (AI)-driven drug discovery in chemistry, including Big Data and advanced Machine Learning, eXplainable AI (XAI), Chemoinformatics, Use of deep learning to predict molecular properties, Modeling and prediction of chemical reaction data and Generative models.
Συγγραφείς: Djork-Arné Clevert, Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko
Resource Type: eBook.
Θέματα: Drug development--Data processing--Congresses, Artificial intelligence--Medical applications--Congresses
Categories: COMPUTERS / Artificial Intelligence / General, COMPUTERS / Data Science / Data Analytics, MATHEMATICS / Probability & Statistics / General, SCIENCE / Chemistry / Computational & Molecular Modeling
Βάση Δεδομένων: eBook Index
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  Data: This open Access book constitutes the refereed proceedings of the First International Workshop on AI in Drug Discovery, AIDD 2024, held as a part of the 33rd International Conference on Artificial Neural Networks, ICANN 2024, in Lugano, Switzerland, on September 19, 2024. The 12 papers presented here were carefully reviewed and selected for these open access proceedings. These papers focus on various aspects of the rapidly evolving field of Artificial Intelligence (AI)-driven drug discovery in chemistry, including Big Data and advanced Machine Learning, eXplainable AI (XAI), Chemoinformatics, Use of deep learning to predict molecular properties, Modeling and prediction of chemical reaction data and Generative models.
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            – D: 01
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              Y: 2025
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